Method for assessing health of wetland ecosystem based on characteristics of benthic animals

Abstract

The application discloses a wetland ecosystem health evaluation method based on characteristics of benthic animals, and is a wetland ecosystem health evaluation method which comprehensively considers the synergistic effect of hydrology, water quality and biological elements, and avoids single group structure and difficulty in comprehensively representing the health state of an ecosystem. The problem that benthic animals as a key functional group of the wetland ecosystem, the community structure and functional traits of which can more sensitively reflect environmental pressure and ecological change and are better fully incorporated into a health evaluation index system is solved.

Description

Technical Field

[0001] This invention relates to the field of wetland ecosystem health assessment technology, and more particularly to a wetland ecosystem health assessment method based on benthic animal characteristics. Background Technology

[0002] Establishing a scientific system for wetland ecosystem health assessment is a crucial foundation for ensuring the stability of wetland ecological functions and achieving ecological protection and sustainable management. Wetland ecosystem health assessment reflects the comprehensive needs for maintaining the stability of wetland biological communities and the integrity of ecological functions, and serves as an important basis for measuring wetland ecological integrity and system resilience.

[0003] Traditional wetland ecosystem health assessments primarily rely on single-indicator methods (such as water quality monitoring) or ecological assessments based on specific biological groups (such as bird or fish populations). However, single-indicator methods often focus only on one aspect of environmental factors, neglecting the synergistic effects between various ecosystem elements; while methods based on specific biological groups struggle to comprehensively reflect the overall functional status of wetland ecosystems. Furthermore, due to the highly dynamic nature of wetland ecosystems and their susceptibility to climate change and human activities, traditional assessment methods often fail to accurately capture the system's complex response mechanisms.

[0004] In recent years, the development of multidimensional ecosystem assessment theories has provided new insights into wetland health assessment, establishing a more scientific and comprehensive evaluation system by coupling environmental factors with ecological response processes. However, while existing methods consider bioindicators, they often focus on higher organisms (such as birds and large aquatic plants) as indicator species, resulting in a single taxonomic structure and the presence of many at specific trophic levels in the food chain, making it difficult to comprehensively characterize the health status of the ecosystem. Benthic animals, as a key functional group in wetland ecosystems, can more sensitively reflect environmental stress and ecological changes through their community structure and functional traits, but they have not yet been fully incorporated into health assessment indicator systems.

[0005] Furthermore, wetland ecosystem health assessment involves complex interactions among multiple factors (hydrology, water quality, biology, and geomorphology). Previous studies have often employed single-dimensional methods (such as water quality standards) or simple weighted methods (such as water quality + biomass scores) for evaluation, but these methods are insufficient to accurately depict the true state of the ecosystem. For example, changes in hydrological conditions may affect the habitat of benthic animals, while fluctuations in nutrient concentrations may alter their community functions. However, a systematic assessment method that comprehensively considers the synergistic effects of hydrological, water quality, and biological factors is currently lacking. Therefore, there is an urgent need to develop a wetland health assessment system based on multi-dimensional ecological indicators (such as benthic animal community structure and functional traits) and to establish an ecosystem health assessment framework that integrates the "environment-biology" synergy to improve the scientific rigor and accuracy of the assessment results. Summary of the Invention

[0006] Therefore, it is necessary to propose a wetland ecosystem health assessment method based on benthic animal characteristics to address the above problems.

[0007] A method for assessing the health of wetland ecosystems based on benthic animal characteristics, the method comprising:

[0008] The target wetland area is acquired by acquiring hydrological data, water environment data, meteorological data, and biodiversity data, and the hydrological data, water environment data, and biodiversity data of the target wetland area are acquired and supplemented to collect benthic animal samples and determine the benthic animal community structure; and a wetland benthic animal species composition information database is established, which includes core fields such as species name, taxonomic status, geographical distribution, habitat type, frequency of occurrence, and abundance.

[0009] The animal species in the wetland benthic animal species composition information database are classified and sorted according to individual size, feeding characteristics, habitat type, metamorphosis, heat resistance and environmental sensitivity to form a wetland benthic animal functional trait information database.

[0010] Based on the aforementioned wetland benthic animal species composition database and wetland benthic animal functional trait database, a combination of hierarchical analysis and cluster analysis was adopted. RLQ triple correlation analysis was used to reveal the multidimensional relationship between ring traits and communities. The PAM algorithm based on Gower distance was used to divide functional groups, and the functional diversity index was calculated to quantify community functional characteristics, thereby determining the wetland ecological indicator system. Species with strong correlations and high density of species and functional traits and their corresponding functional traits were selected from the wetland ecological indicator system as experimental subjects.

[0011] Identify the key regulatory factors of the species and corresponding functional traits in different types of marsh wetlands, including hydrological data and water environment data;

[0012] The habitat suitability of the target wetland area is determined based on the key regulatory factors.

[0013] The hydrological health index, water quality suitability index, and biological health index are obtained, and the health index of the target wetland area is determined by combining the habitat suitability.

[0014] In one embodiment, the hydrological health index is obtained as follows:

[0015] Obtain the indicator weights and standardized hydrological indicator values;

[0016] The hydrological health index is obtained by summing the product of the index weights and the standardized hydrological index values.

[0017] In one embodiment, the biohealth index is obtained as follows:

[0018] Obtain the relative degree of use of species t for the kth trait under the i-th functional trait; determine the species trait index of species t for the i-th functional attribute based on the relative degree of use;

[0019] The modified species characteristic index is obtained by normalizing and correcting the species characteristic index. ; for the modified species characteristic index Standardized species characteristic indices are obtained by standardization processing;

[0020] The biological health index is determined based on the standardized species characteristic index.

[0021] In one embodiment, determining the habitat suitability of the target wetland area based on the key regulatory factors is achieved through the following expression:

[0022]

[0023] In the formula, Habitat suitability is an ecological indicator for benthic animals. The intercept term representing the model, and As a key regulatory factor, This represents the quantitative relationship between key regulatory factors and ecological indicators.

[0024] In one embodiment, the hydrological health index is implemented by the following expression:

[0025]

[0026]

[0027] in, The weights of the indicators are determined using the analytic hierarchy process. These are the standardized hydrological index values; This includes hydrological data, water environment data, meteorological data, or biodiversity data. It is the minimum value of hydrological data, water environment data, meteorological data, or biodiversity data; The maximum value of hydrological data, water environment data, meteorological data, or biodiversity data; This refers to the hydrological health index.

[0028] In one embodiment, the modified species trait index is implemented by the following expression:

[0029]

[0030]

[0031]

[0032] in, Let represent the relative degree of use of the k-th trait of species t under the i-th functional trait; Let N be the number of the k-th trait of species t under the i-th functional attribute; N is the total number of species t. Let be the species characteristic index of species t for the i-th functional attribute. This is a species characteristic index after normalization correction. The minimum value of the species characteristic index; This represents the maximum value of the species characteristic index.

[0033] In one embodiment, the biohealth index is implemented by the following expression:

[0034]

[0035] Among them, TSI K 'N' is a standardized species characteristic index. K λ is the number of individuals in the k-th functional group, m is the number of functional groups; λ is the function stability coefficient, λ=min(f'm) / max(f'm); w is the weight coefficient determined by the AHP-entropy weight method.

[0036] In one embodiment, the acquisition of hydrological health index and biological health index, and the determination of the health index of the target wetland area in combination with the habitat suitability, are achieved by the following expression:

[0037]

[0038]

[0039] in, An index of ecosystem health; , and All are weighting coefficients; The water quality suitability index; λ is the biological health index; λ is the function stability coefficient. Hydrological health index; Habitat suitability.

[0040] This invention is a wetland ecosystem health assessment method that comprehensively considers the synergistic effects of hydrological, water quality, and biological elements, avoiding the limitations of a single taxonomic structure that makes it difficult to fully characterize the ecosystem's health status. It addresses the issue that benthic animals, as a key functional group in wetland ecosystems, can more sensitively reflect environmental stress and ecological changes through their community structure and functional traits, thus enabling their better and more comprehensive inclusion in the health assessment indicator system. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] in:

[0043] Figure 1 This is an application environment diagram of a wetland ecosystem health assessment method based on benthic animal characteristics in one embodiment;

[0044] Figure 2 This is a flowchart of a wetland ecosystem health assessment method based on benthic animal characteristics in one embodiment;

[0045] Figure 3 This is a structural block diagram of a computer device in one embodiment. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] To address the technical problems in the background art, this application provides a wetland ecosystem health assessment method based on benthic animal characteristics.

[0048] Figure 1 This is an environmental map illustrating the application of a wetland ecosystem health assessment method based on benthic animal characteristics in one embodiment. (Refer to...) Figure 1This method for assessing the health of wetland ecosystems based on benthic animal characteristics is applied to a wetland ecosystem health assessment system based on benthic animal characteristics. This system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal; the mobile terminal can be at least one of a mobile phone, tablet, or laptop. The server 120 can be a standalone server or a server cluster consisting of multiple servers. Terminal 110 is used to acquire target wetland areas encompassing hydrological data, water environment data, meteorological data, and biodiversity data, and to supplement the hydrological, water environment, and biodiversity data of the target wetland areas for benthic animal sample collection and determination of benthic animal community structure; and to establish a wetland benthic animal species composition information database containing core fields such as species name, taxonomic status, geographical distribution, habitat type, frequency of occurrence, and abundance; server 120 is used to classify and organize the animal species in the wetland benthic animal species composition information database according to individual size, feeding characteristics, habitat type, metamorphosis, heat resistance, and environmental sensitivity to form a wetland benthic animal functional trait information database; based on the wetland benthic animal species composition information database and the wetland benthic animal functional trait information database, A trait information database was established, employing a combination of hierarchical and stratified analysis and cluster analysis. RLQ triple correlation analysis was used to reveal the multidimensional relationships between ring traits and communities. The PAM algorithm based on Gower distance was used for functional group division, and the functional diversity index was calculated to quantify community functional characteristics, thereby determining the wetland ecological indicator system. Species with strong correlations and high density of functional traits and their corresponding functional traits were selected from the wetland ecological indicator system as experimental subjects. Key regulatory factors for these species and their corresponding functional traits in different types of marsh wetlands were identified. These key regulatory factors included hydrological data and water environment data. The habitat suitability of the target wetland area was determined based on the key regulatory factors. Hydrological health index, water quality suitability index, and biological health index were obtained, and combined with the habitat suitability, the health index of the target wetland area was determined.

[0049] like Figure 2 As shown, in one embodiment, a method for assessing the health of a wetland ecosystem based on benthic animal characteristics is provided. This method can be applied to both terminals and servers; this embodiment illustrates its application to a terminal. The specific steps of this wetland ecosystem health assessment method based on benthic animal characteristics are as follows:

[0050] S1: Acquire target wetland areas encompassing hydrological data, water environment data, meteorological data, and biodiversity data; acquire the hydrological data, water environment data, and biodiversity data of the target wetland areas; supplement the hydrological data, water environment data, and biodiversity data to collect benthic animal samples and determine the benthic animal community structure; and establish a wetland benthic animal species composition information database containing core fields such as species scientific name (including Latin name), taxonomic status, geographical distribution (specific to latitude and longitude coordinates), habitat type (e.g., swamp, river, lake, etc.), frequency of occurrence, and abundance.

[0051] S2: The animal species in the wetland benthic animal species composition information database are classified and sorted according to individual size (small, medium, large, extra-large), feeding characteristics (collective feeding, filter feeding, scraping feeding, predatory feeding, tearing feeding), habitat type (nesting, climbing, creeping, adhesive, swimming, skating), morphology (semi-morphic, monomorphic, polymorphic), heat resistance (cold-loving, warm-loving, no temperature preference), and environmental sensitivity (environmentally sensitive species, moderately sensitive species, environmentally pollution-tolerant species) to form a wetland benthic animal functional trait information database;

[0052] S3: Based on the aforementioned wetland benthic animal species composition information database and wetland benthic animal functional trait information database, a combination of hierarchical analysis and cluster analysis was adopted. RLQ triple correlation analysis was used to reveal the multidimensional relationship between ring traits and communities. The PAM algorithm based on Gower distance was used to divide functional groups, and functional diversity indices (FRic, FEve, FDiv) were calculated to quantify community functional characteristics to determine the wetland ecological indicator system. Species with strong correlation and high density of species and functional traits and their corresponding functional traits were selected from the aforementioned wetland ecological indicator system as experimental subjects.

[0053] S4: Identify the key regulatory factors of the species and corresponding functional traits in different types of marsh wetlands, including hydrological data and water environment data;

[0054] S5: Determine the habitat suitability of the target wetland area based on the key regulatory factors;

[0055] S6: Obtain the hydrological health index, water quality suitability index, and biological health index, and determine the health index of the target wetland area in combination with the habitat suitability.

[0056] This invention is a wetland ecosystem health assessment method that comprehensively considers the synergistic effects of hydrological, water quality, and biological elements, avoiding the limitations of a single taxonomic structure that makes it difficult to fully characterize the ecosystem's health status. It addresses the issue that benthic animals, as a key functional group in wetland ecosystems, can more sensitively reflect environmental stress and ecological changes through their community structure and functional traits, thus enabling their better and more comprehensive inclusion in the health assessment indicator system.

[0057] In one embodiment, for the hydrological health index in step S6 Obtained through the following methods:

[0058] S601: Obtain Indicator Weights and standardized hydrological index values ;

[0059] S602: Weighting the aforementioned indicators and standardized hydrological index values The hydrological health index is obtained by summing the data from the flights. .

[0060] In one embodiment, for the bio-health index in step S6 Obtained through the following methods:

[0061] S603: Obtain the relative degree of use of species t for the kth trait under the i-th functional trait; determine the species trait index of species t for the i-th functional attribute based on the relative degree of use;

[0062] S604: Normalize the species characteristic index to obtain a modified species characteristic index; adjust the modified species characteristic index... Standardized species characteristic indices are obtained by standardization processing;

[0063] S605: Determine the biological health index based on the standardized species characteristic index.

[0064] In one embodiment, the step S5 of determining the habitat suitability of the target wetland area based on the key regulatory factors... This can be achieved using the following expression:

[0065] (1)

[0066] In the formula, Habitat suitability is an ecological indicator for benthic animals. The intercept term representing the model, and As a key regulatory factor, This represents the quantitative relationship between key regulatory factors and ecological indicators.

[0067] In one embodiment, for the hydrological health index in step S6 This can be achieved using the following expression:

[0068] (2)

[0069] (3)

[0070] in, The weights of the indicators are determined using the analytic hierarchy process. These are the standardized hydrological index values; This includes hydrological data, water environment data, meteorological data, or biodiversity data. It is the minimum value of hydrological data, water environment data, meteorological data, or biodiversity data; The maximum value of hydrological data, water environment data, meteorological data, or biodiversity data; This refers to the hydrological health index.

[0071] In one embodiment, for the modified species characteristic index in step S604 This can be achieved using the following expression:

[0072] (4)

[0073] (5)

[0074] (6)

[0075] in, Let represent the relative degree of use of the k-th trait of species t under the i-th functional trait; Let N be the number of the k-th trait of species t under the i-th functional attribute; N is the total number of species t. Let be the species characteristic index of species t for the i-th functional attribute. This is a species characteristic index after normalization correction. The minimum value of the species characteristic index; This represents the maximum value of the species characteristic index.

[0076] In one embodiment, for the bio-health index in step S6 This can be achieved using the following expression:

[0077] (7)

[0078] in, N is a standardized species characteristic index. K λ is the number of individuals in the k-th functional group, m is the number of functional groups; λ is the function stability coefficient, λ=min(f'm) / max(f'm); w is the weight coefficient determined by the AHP-entropy weight method; AHP reflects expert experience judgment (subjective weight), while the entropy weight method reflects the objective laws of data (objective weight). By combining methods, the bias that may be generated by a single method can be reduced (AHP is easily affected by subjectivity, while the entropy weight method ignores professional cognition).

[0079] In one embodiment, for the acquisition of the hydrological health index in step S6 and biological health index In conjunction with the aforementioned habitat suitability Determine the health index of the target wetland area. This can be achieved using the following expression:

[0080] (8)

[0081] (9)

[0082] in, An index of ecosystem health; , and All are weighting coefficients; The water quality suitability index; λ is the biological health index; λ is the function stability coefficient. This refers to the hydrological health index.

[0083] The study aims to acquire data on the target wetland area, including hydrology, water environment, meteorology, and biodiversity. It also collects long-term data on existing water quality, hydrology, and biological elements in the target area. For areas lacking existing data, it conducts systematic field surveys with seasonal frequency. Supplementary surveys include indicators related to water bodies and sediments (pH, dissolved oxygen, water temperature, turbidity, conductivity, etc.). Benthic animal samples are collected and analyzed to determine the benthic animal community structure.

[0084] Based on the existing information and supplementary survey data, the distribution characteristics of benthic flora in the target area were analyzed. A modern taxonomic system (referencing authoritative databases such as the World Register of Marine Species and Catalogue of Life) was used to systematically classify benthic animals, calculating biodiversity indices (Shannon-Wiener diversity index (H'), evenness index (J), and Margalef richness index (d)). The data was categorized according to the hierarchical structure of kingdom, phylum, class, order, family, genus, and species. To ensure data quality, species identification results were calibrated by comparing with international databases such as GBIF and BOLD Systems. Furthermore, the alpha diversity index was used to assess the species coverage of each habitat type. Based on these operations, a wetland benthic animal species composition database was established, containing core fields such as species name (including Latin name), taxonomic position, geographical distribution (down to latitude and longitude coordinates), habitat type (e.g., swamp, river, lake), frequency of occurrence, and abundance.

[0085] Based on the aforementioned wetland benthic animal species composition information database, this system integrates species habit data from published domestic and international literature, regional zoographies, and authoritative databases. It categorizes and organizes species according to factors including individual size (small, medium, large, extra-large), feeding characteristics (collective feeding, filter feeding, scraping feeding, predatory feeding, tearing feeding), habitat type (nesting, climbing, creeping, adhesive, swimming, skating), metamorphosis (semi-metamorphic, monometamorphic, polymetamorphic), heat resistance (cold-loving, warm-loving, no temperature preference), and environmental sensitivity (environmentally sensitive, moderately sensitive, pollution-tolerant). This results in a functional trait information database for wetland benthic animals.

[0086] Based on the aforementioned wetland benthic animal species composition database and wetland benthic animal functional trait database, this study employs a combination of hierarchical analysis and cluster analysis. RLQ triple correlation analysis reveals the multidimensional relationships between ring traits and communities. The PAM algorithm based on Gower distance is used to divide functional groups, and functional diversity indices (FRic, FEve, FDiv) are calculated to quantify community functional characteristics, thereby determining the wetland ecological indicator system. Species and functional traits with strong correlations and high density are selected from the wetland ecological indicator system as experimental subjects.

[0087] Spatial zoning of wetland types in the target area. Based on the degree of wetland degradation, the target area is spatially divided into three typical types: [List of types would be inserted here]

[0088] 1) Near-natural wetlands (vegetation coverage > 85%, good hydrological connectivity, human disturbance index < 0.2);

[0089] 2) Moderately disturbed wetlands (vegetation coverage 40-85%, moderate disruption of hydrological connectivity, human disturbance index 0.2-0.5);

[0090] 3) Severely disturbed wetlands (vegetation coverage < 40%, severe damage to hydrological connectivity, human disturbance index > 0.5).

[0091] To address spatiotemporal variability analysis, this invention employs a two-dimensional research methodology focusing on seasonal dynamics and spatial heterogeneity. In seasonal dynamics analysis, Similarity Percentage Analysis (SIMPER) is used to quantitatively assess the variation patterns of environmental factors in spring, summer, and autumn. By calculating the similarity coefficients and contribution rates of environmental factors within each group, the dominant environmental factors for each season are identified. For spatial heterogeneity analysis, Principal Component Analysis (PCA) is used. After data standardization and principal component extraction, the environmental factors with the primary influence at different spatial locations are determined. The smaller the angle formed by the line connecting the location to the center of the circle and the environmental factor vector, the more dominant the environmental factor's influence at that location. Based on this, the dominant environmental factors are identified.

[0092] Based on the database of major environmental factors and benthic animal species composition and functional diversity information obtained from the target area, multivariate statistical methods were used to systematically analyze the correlation mechanism between environmental factors and benthic animal communities. First, the DistLM (Distance-based Linear Model) analysis model was used to analyze the linear relationship between hydrological and aquatic environmental parameters and benthic animal species diversity (Shannon index) and functional diversity (FRic index), and the optimal model was selected using the AICc criterion. Simultaneously, the BEST analysis model was used to fit nonlinear relationships. By comparing seven similarity measures, including Euclidean distance and Bray-Curtis distance, the BIO-ENV procedure was used to determine the optimal combination of environmental factors, and the Spearman rank correlation coefficient (ρ value) was calculated to assess explanatory power. The combined use of these two methods achieved a comprehensive analysis of the environment-biological correlation mechanism. Finally, by synthesizing the above correlation analysis results, key hydrological and aquatic environmental regulatory factors for different types of wetlands were identified.

[0093] To study key hydrological and aquatic environment regulatory factors in wetlands, different input scenarios were set up in a field plot simulation experimental area to conduct in-situ control experiments using a plot-microcosm approach. The field plot control experimental area allowed for the control of hydrological and aquatic environment input conditions. Each plot ranged in size from 2m×2m to 3m×3m (five parallel plots were set up for each plot in the plot simulation). All plots were randomly arranged and isolated by 1.2m PVC panels (0.8m of which was embedded underground) to prevent lateral water seepage. A buffer zone of at least 1m was established between plots. The experimental period was set from May to October (months with relatively high diversity of most benthic animals).

[0094] Sampling is conducted monthly, and the monitoring content and methods are the same as those in the field systematic survey, mainly including hydrological, water quality parameters and benthic animal biodiversity data.

[0095] Based on hydrological data, aquatic environment data, and species diversity data, five data matrices can be constructed as follows: hydrological data matrix a, aquatic environment data matrix b, species distribution matrix c, species characteristic attribute matrix d, and characteristic comprehensive matrix e. The species distribution and functional traits are determined after screening ecological indicators based on key species and core functional traits. Matrices a, b, and c are processed directly and simply by filling in the corresponding location names, hydrological parameters, aquatic environment parameters, and the number of species. Matrix d requires combining the species names and corresponding attribute information from matrix c. This study intends to select six functional attributes that may influence changes in hydrological conditions, including benthic animal individual size, feeding characteristics, behavioral habitat, insect metamorphosis, heat resistance, and environmental sensitivity, for classification and statistical analysis. If a species possesses the relevant attribute characteristics, it is counted as "1"; otherwise, it is counted as "0". The correspondence between species and functional traits is based on the integration of existing domestic and international insect information websites (many benthic animal species are distributed both domestically and internationally, and foreign insect information websites can supplement the information on relevant domestic benthic species) and existing published literature (domestic and international studies on the functional attributes of benthic animals); matrix e is obtained by multiplying matrix d by the logarithmic transformation of the number of species distributions in matrix c. The data matrix is ​​shown in Table 1.

[0096] Table 1 Data Matrix

[0097]

[0098] Finally, a five-level classification system was established based on the wetland ecosystem health assessment index:

[0099] Excellent health (WEHI≥0.85): All fm(xm) functions exhibit ideal curve shapes, and the system is within the natural fluctuation range. Typical characteristics include a biodiversity index Shannon H'>3.0 and a sensitive species ratio ≥30%.

[0100] Good state (0.70≤WEHI<0.85): No more than two minor functions show slight deviations, and the system maintains basic functionality. This is characterized by the number of critical species remaining above 80% of the reference system.

[0101] Moderate degradation (0.50≤WEHI<0.70): Core functions such as the hydrological periodic function change significantly, requiring human intervention. This is often accompanied by simplification of functional group structure, with the FRic index decreasing by >40%.

[0102] Severe degradation (0.30≤WEHI<0.50): Abnormal relationships among multiple functions, and partial loss of ecological functions. Typical manifestations include a pollution-tolerant species ratio exceeding 60% and a biomass reduction of more than 50%.

[0103] Inferior state (WEHI < 0.30): Functional relationships collapse, and the system struggles to maintain itself. An ecosystem reconstruction project is required.

[0104] Figure 3 An internal structural diagram of a computer device in one embodiment is shown. This computer device can specifically be a terminal or a server. Figure 3 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement a wetland ecosystem health assessment method based on benthic animal characteristics. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to implement the wetland ecosystem health assessment method based on benthic animal characteristics. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0105] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0106] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for assessing the health of wetland ecosystems based on benthic animal characteristics, characterized in that, The method includes: The target wetland area is acquired by acquiring hydrological data, water environment data, meteorological data, and biodiversity data, and the hydrological data, water environment data, and biodiversity data of the target wetland area are acquired and supplemented to collect benthic animal samples and determine the benthic animal community structure; and a wetland benthic animal species composition information database is established, which includes core fields such as species name, taxonomic status, geographical distribution, habitat type, frequency of occurrence, and abundance. The animal species in the wetland benthic animal species composition information database are classified and sorted according to individual size, feeding characteristics, habitat type, metamorphosis, heat resistance and environmental sensitivity to form a wetland benthic animal functional trait information database. Based on the aforementioned wetland benthic animal species composition database and wetland benthic animal functional trait database, a combination of hierarchical analysis and cluster analysis was adopted. RLQ triple correlation analysis was used to reveal the multidimensional relationship between ring traits and communities. The PAM algorithm based on Gower distance was used to divide functional groups, and the functional diversity index was calculated to quantify community functional characteristics, thereby determining the wetland ecological indicator system. Species with strong correlations and high density of species and functional traits and their corresponding functional traits were selected from the wetland ecological indicator system as experimental subjects. Identify the key regulatory factors of the species and corresponding functional traits in different types of marsh wetlands, including hydrological data and water environment data; The habitat suitability of the target wetland area is determined based on the key regulatory factors. The hydrological health index, water quality suitability index, and biological health index are obtained, and the health index of the target wetland area is determined by combining the habitat suitability.

2. The wetland ecosystem health assessment method based on benthic animal characteristics according to claim 1, characterized in that, The hydrological health index is obtained through the following method: Obtain the indicator weights and standardized hydrological indicator values; The hydrological health index is obtained by summing the product of the index weights and the standardized hydrological index values.

3. The wetland ecosystem health assessment method based on benthic animal characteristics according to claim 1, characterized in that, The biohealth index is obtained through the following methods: Obtain the relative degree of use of species t for the kth trait under the i-th functional trait; determine the species trait index of species t for the i-th functional attribute based on the relative degree of use; The modified species characteristic index is obtained by normalizing and correcting the species characteristic index. ; for the modified species characteristic index Standardized species characteristic indices are obtained by standardization processing; The biological health index is determined based on the standardized species characteristic index.

4. The wetland ecosystem health assessment method based on benthic animal characteristics according to claim 1, characterized in that, The determination of habitat suitability of the target wetland area based on the key regulatory factors is achieved through the following expression: In the formula, Habitat suitability is an ecological indicator for benthic animals. The intercept term representing the model, and As a key regulatory factor, This represents the quantitative relationship between key regulatory factors and ecological indicators.

5. The wetland ecosystem health assessment method based on benthic animal characteristics according to claim 2, characterized in that, The hydrological health index is expressed by the following formula: in, The weights of the indicators are determined using the analytic hierarchy process. These are the standardized hydrological index values; This includes hydrological data, water environment data, meteorological data, or biodiversity data. It is the minimum value of hydrological data, water environment data, meteorological data, or biodiversity data; The maximum value of hydrological data, water environment data, meteorological data, or biodiversity data; This refers to the hydrological health index.

6. The wetland ecosystem health assessment method based on benthic animal characteristics according to claim 3, characterized in that, The modified species characteristic index is achieved through the following expression: in, Let represent the relative degree of use of the k-th trait of species t under the i-th functional trait; Let N be the number of the k-th trait of species t under the i-th functional attribute; N is the total number of species t. Let be the species characteristic index of species t for the i-th functional attribute. This is a species characteristic index after normalization correction. This represents the minimum value of the species characteristic index; This represents the maximum value of the species characteristic index.

7. The wetland ecosystem health assessment method based on benthic animal characteristics according to claim 6, characterized in that, The biohealth index is expressed by the following formula: in, To standardize species characteristic indices, N K Let m be the number of individuals in the k-th functional group, and m be the number of functional groups.

8. The wetland ecosystem health assessment method based on benthic animal characteristics according to claim 1, characterized in that, The process of obtaining the hydrological health index and biological health index, and determining the health index of the target wetland area in conjunction with the habitat suitability, is achieved through the following expression: in, An index of ecosystem health; , and All are weighting coefficients; The water quality suitability index; λ is the biological health index; λ is the function stability coefficient, λ=min(f'm) / max(f'm); Hydrological health index; Habitat suitability.

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